You want to tell your reader what
type of analysis you conducted. This will help your reader make sense of
your results. You also want to tell your reader why this particular analysis
was used. What did your analysis tests for?

In the previous chapter on
interpretation, you learned that the significance value generated in a 1-Way
Within Subjects ANOVA doesn’t tell you everything. If you find a significant
effect using this type of test, you can conclude that there is a significant
difference between some of the conditions in your experiment. However, you
will not know where this effect exists. The significant difference could be
between any or all of the conditions in your experiment. In the previous
chapter, you learned that to determine where significance exists you need to
conduct a series of paired samples t-tests to compare each condition with
all other conditions. If you have an IV with 3 levels, like the one in this
example, you would need to conduct and report the results of three
additional paired samples t-tests. Remember that you are using the number
0.017 to determine statistical significance, rather than .05, because you
are doing three tests instead of just one (05/3 = 0.017).

Because we have found a
statistically significant result in this example, we needed to compute three
additional paired samples t-tests. We used one Paired Samples T-Test to
compare just the caffeine and juice conditions. A second Paired Samples
T-Test to compare just the caffeine and beer conditions. And a third Paired
Samples T-Test to compare just the juice and beer conditions. The results of
these three tests must be reported. In the paired samples t-test chapter,
you learned how to report the results of such tests. Here’s what the results
of our three Paired Samples T-tests might look like.

“Three paired samples t-tests were
used to make post hoc comparisons between conditions. A first paired samples
t-test indicated that there was a significant difference in the scores for
caffeine (M=5.4, SD=1.14) and beer (M=9.4, SD=1.14) conditions; t(4)=-5.66,
p = .005. A second paired samples t-test indicated that there was a
significant difference in the scores for caffeine (M=5.4, SD=1.14) and juice
(M=7.2, SD=1.10) conditions; t(4)=-4.881, p = .009. A third paired samples
t-test indicated that there was no significant difference in the scores for
juice (M=7.2, SD=1.10) and beer (M=9.4, SD=1.14) conditions; t(4)=-3.773, p
= .02.”

Since it might be hard for someone
to figure out what that sentence means or how it relates to your experiment,
you want to briefly recap in words that people can understand. Try to
imagine trying to explain your results to someone who is not familiar with
science. In one sentence, explain your results in easy to understand
language.

“These results suggest that beverage
type really does have an effect on hours of sleep. Specifically, our results
suggest that when humans drink caffeine, they sleep significantly less than
when they drink juice and when they drink beer. However, there is no real
difference in hours slept when comparing juice and beer consumption.”

This sentence is so much easier to
understand than the scientific one with all of the numbers in it.

When you put the three main
components together, results look something like this.

“A one-way within subjects (or
repeated measures) ANOVA was conducted to compare the effect of beverage
type on number of hours slept in caffeine, juice and beer conditions. There
was a significant effect of beverage type, Wilks’ Lambda = 0.10, F (2,3) =
13.42, p = .032. Three paired samples t-tests were used to make post hoc
comparisons between conditions. A first paired samples t-test indicated that
there was a significant difference in the scores for caffeine (M=5.4,
SD=1.14) and beer (M=9.4, SD=1.14) conditions; t(4)=-5.66, p = .005. A
second paired samples t-test indicated that there was a significant
difference in the scores for caffeine (M=5.4, SD=1.14) and juice (M=7.2,
SD=1.10) conditions; t (4)=-4.881, p = .009. A third paired samples t-test
indicated that there was no significant difference in the scores for juice
(M=7.2, SD=1.10) and beer (M=9.4, SD=1.14) conditions; t (4)=-3.773, p =
.02.. These results suggest that beverage type really does have an effect on
hours of sleep. Specifically, our results suggest that when humans drink
caffeine, they sleep significantly less than when they drink juice and when
they drink beer. However, there is no real difference in hours slept when
comparing juice and beer consumption.”

Looks pretty
complicated but it is simple when you know how to write each part.